To recognize a handwritten input character, various types of recognition models may be applied for classification purposes, such as an online recognition model (e.g., a Hidden Markov Model) or an offline recognition model (e.g., a statistical template-based model).
However, different error sets result from different types of recognition models. As a result, while both types of recognition models provide very good classification performance, the models have different error cases on a given dataset and thus the recognition accuracy suffers to an extent depending on the dataset.